OpenRaiser/Intern-Atlas huggingface dataset:
Raising Awareness of Conveyed Personality In Social Media Traces
Venue: arXiv cs.HC 2017
Year: 2017

Users' persistent social media contents like posts on Facebook Timeline are presented as an "exhibition" about the person to others, and managing these exhibitional contents for impression management needs intentional and manual efforts. To raise awareness of and facilitate impression management around past contents, we developed a prototype called PersonalityInsight. The system employs computational psycho-linguistic analysis to help users visualize the way their past text posts might convey impressions of their personality and allowed users to modify their posts based on these visualizations. We conducted a user study to evaluate the design; users overall found that such a tool raised awareness of the fact and the ways personality might be conveyed through their past content as one aspect of impression management, but that it needs design improvement to offer action-able suggestions for content modification, as well as careful thinking about impression management as one of many values people have about their digital past.
Harvested Power Maximization in QoS-Constrained MIMO SWIPT with Generic RF Harvesting Model
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of maximizing the harvested power in Multiple Input Multiple Output (MIMO) Simultaneous Wireless Information and Power Transfer (SWIPT) systems with power splitting reception. Different from recently proposed designs, we target with our novel problem formulation at the jointly optimal transmit precoding and receive uniform power splitting (UPS) ratio maximizing the harvested power, while ensuring that the Quality-of-Service (QoS) requirement of the MIMO link is satisfied. We assume generic practical Radio Frequency (RF) Energy Harvesting (EH) receive operation that results in a non-convex optimization problem for the design parameters, which we then solve optimally after formulating it in an equivalent generalized convex form. Our representative results including comparisons of achievable EH gains with benchmark schemes provide key insights on various system parameters.
Sign-Constrained Regularized Loss Minimization
Venue: arXiv cs.LG 2017
Year: 2017

In practical analysis, domain knowledge about analysis target has often been accumulated, although, typically, such knowledge has been discarded in the statistical analysis stage, and the statistical tool has been applied as a black box. In this paper, we introduce sign constraints that are a handy and simple representation for non-experts in generic learning problems. We have developed two new optimization algorithms for the sign-constrained regularized loss minimization, called the sign-constrained Pegasos (SC-Pega) and the sign-constrained SDCA (SC-SDCA), by simply inserting the sign correction step into the original Pegasos and SDCA, respectively. We present theoretical analyses that guarantee that insertion of the sign correction step does not degrade the convergence rate for both algorithms. Two applications, where the sign-constrained learning is effective, are presented. The one is exploitation of prior information about correlation between explanatory variables and a target variable. The other is introduction of the sign-constrained to SVM-Pairwise method. Experimental results demonstrate significant improvement of generalization performance by introducing sign constraints in both applications.
Shortcuts through Colocation Facilities
Venue: arXiv cs.NI 2017
Year: 2017

Network overlays, running on top of the existing Internet substrate, are of perennial value to Internet end-users in the context of, e.g., real-time applications. Such overlays can employ traffic relays to yield path latencies lower than the direct paths, a phenomenon known as Triangle Inequality Violation (TIV). Past studies identify the opportunities of reducing latency using TIVs. However, they do not investigate the gains of strategically selecting relays in Colocation Facilities (Colos). In this work, we answer the following questions: (i) how Colo-hosted relays compare with other relays as well as with the direct Internet, in terms of latency (RTT) reductions; (ii) what are the best locations for placing the relays to yield these reductions. To this end, we conduct a large-scale one-month measurement of inter-domain paths between RIPE Atlas (RA) nodes as endpoints, located at eyeball networks. We employ as relays Planetlab nodes, other RA nodes, and machines in Colos. We examine the RTTs of the overlay paths obtained via the selected relays, as well as the direct paths. We find that Colo-based relays perform the best and can achieve latency reductions against direct paths, ranging from a few to 100s of milliseconds, in 76% of the total cases; 75% (58% of total cases) of these reductions require only 10 relays in 6 large Colos.
Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR
Venue: arXiv cs.CL 2017
Year: 2017

This thesis introduces the sequence to sequence model with Luong's attention mechanism for end-to-end ASR. It also describes various neural network algorithms including Batch normalization, Dropout and Residual network which constitute the convolutional attention-based seq2seq neural network. Finally the proposed model proved its effectiveness for speech recognition achieving 15.8% phoneme error rate on TIMIT dataset.
Is Epicurus the father of Reinforcement Learning?
Venue: arXiv cs.LG 2017
Year: 2017

The Epicurean Philosophy is commonly thought as simplistic and hedonistic. Here I discuss how this is a misconception and explore its link to Reinforcement Learning. Based on the letters of Epicurus, I construct an objective function for hedonism which turns out to be equivalent of the Reinforcement Learning objective function when omitting the discount factor. I then discuss how Plato and Aristotle 's views that can be also loosely linked to Reinforcement Learning, as well as their weaknesses in relationship to it. Finally, I emphasise the close affinity of the Epicurean views and the Bellman equation.
Cluster-based Position Tracking of Mobile Sensors
Venue: arXiv cs.NI 2017
Year: 2017

Tracking movement of mobile nodes has received significant scientific and commercial interest, but long term tracking of resource-constrained mobile nodes remains challenging due to the high energy consumption of satellite receivers. Cooperative position tracking has been proposed for energy efficiency, however, all the cooperative schemes use opportunistic cooperation and optimize for either energy or accuracy. Considering the existence of a reasonably stable group of mobile nodes like animals, birds, and mobile assets, we propose a cluster-based cooperative tracking algorithm, where cluster head centrally coordinates resource usage among cluster members. Variants of this strategy include the use of a cooperative Kalman filter with and without inertial sensor inputs to estimate nodes positions. We use the Boid flocking algorithm to generate group position movements in 3D and perform various experiments to compare the energy and position accuracy tradeoff of our proposed scheme with individual-based tracking and existing cooperative schemes. We perform the experiments for fixed periodic GPS sampling and dynamic GPS sampling triggered by node position error uncertainty tolerance limit. Experiments results show that in periodic sampling scheme cooperative tracking yields more than one-quarter reduction in energy consumption and more than one-third improvement in position accuracy over individual-based tracking, however, results for dynamic sampling scheme are comparable with existing cooperative scheme.
A Topological proof that $O_2$ is $2$-MCFL
Venue: arXiv cs.FL 2017
Year: 2017

We give a new proof of Salvati's theorem that the group language $O_2$ is $2$ multiple context free. Unlike Salvati's proof, our arguments do not use any idea specific to two-dimensions. This raises the possibility that the argument might generalize to $O_n$.
Auto Analysis of Customer Feedback using CNN and GRU Network
Venue: arXiv cs.CL 2017
Year: 2017

Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is in great demand. Users may write feedbacks in any language, and hence mining appropriate information often becomes intractable. Especially in a traditional feature-based supervised model, it is difficult to build a generic system as one has to understand the concerned language for finding the relevant features. In order to overcome this, we propose deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches that do not require handcrafting of features. We evaluate these techniques for analyzing customer feedback sentences in four languages, namely English, French, Japanese and Spanish. Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis. Our model achieved the second rank in French, with an accuracy of 71.75% and third ranks for all the other languages.
Analysis of planar ornament patterns via motif asymmetry assumption and local connections
Venue: arXiv cs.CV 2017
Year: 2017

Planar ornaments, a.k.a. wallpapers, are regular repetitive patterns which exhibit translational symmetry in two independent directions. There are exactly $17$ distinct planar symmetry groups. We present a fully automatic method for complete analysis of planar ornaments in $13$ of these groups, specifically, the groups called $p6m, \, p6, \, p4g, \,p4m, \,p4, \, p31m, \,p3m, \, p3, \, cmm, \, pgg, \, pg, \, p2$ and $p1$. Given the image of an ornament fragment, we present a method to simultaneously classify the input into one of the $13$ groups and extract the so called fundamental domain (FD), the minimum region that is sufficient to reconstruct the entire ornament. A nice feature of our method is that even when the given ornament image is a small portion such that it does not contain multiple translational units, the symmetry group as well as the fundamental domain can still be defined. This is because, in contrast to common approach, we do not attempt to first identify a global translational repetition lattice. Though the presented constructions work for quite a wide range of ornament patterns, a key assumption we make is that the perceivable motifs (shapes that repeat) alone do not provide clues for the underlying symmetries of the ornament. In this sense, our main target is the planar arrangements of asymmetric interlocking shapes, as in the symmetry art of Escher.
Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features
Venue: arXiv cs.RO 2017
Year: 2017

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in long-duration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this concept through mid-depth, underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local, ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller-scale, turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved, small-scale flow perturbations are present. For a 6-hour navigation with an automotive-grade inertial navigation system, the current-aided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a turbulent, meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-hour mission, positioning result with under 25% UDT for a 24-hour navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.
Dropout as a Low-Rank Regularizer for Matrix Factorization
Venue: arXiv cs.LG 2017
Year: 2017

Regularization for matrix factorization (MF) and approximation problems has been carried out in many different ways. Due to its popularity in deep learning, dropout has been applied also for this class of problems. Despite its solid empirical performance, the theoretical properties of dropout as a regularizer remain quite elusive for this class of problems. In this paper, we present a theoretical analysis of dropout for MF, where Bernoulli random variables are used to drop columns of the factors. We demonstrate the equivalence between dropout and a fully deterministic model for MF in which the factors are regularized by the sum of the product of squared Euclidean norms of the columns. Additionally, we inspect the case of a variable sized factorization and we prove that dropout achieves the global minimum of a convex approximation problem with (squared) nuclear norm regularization. As a result, we conclude that dropout can be used as a low-rank regularizer with data dependent singular-value thresholding.
An adaptive thresholding approach for automatic optic disk segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Optic disk segmentation is a prerequisite step in automatic retinal screening systems. In this paper, we propose an algorithm for optic disk segmentation based on a local adaptive thresholding method. Location of the optic disk is validated by intensity and average vessel width of retinal images. Then an adaptive thresholding is applied on the temporal and nasal part of the optic disc separately. Adaptive thresholding, makes our algorithm robust to illumination variations and various image acquisition conditions. Moreover, experimental results on the DRIVE and KHATAM databases show promising results compared to the recent literature. In the DRIVE database, the optic disk in all images is correctly located and the mean overlap reached to 43.21%. The optic disk is correctly detected in 98% of the images with the mean overlap of 36.32% in the KHATAM database.
Link Before You Share: Managing Privacy Policies through Blockchain
Venue: arXiv cs.CR 2017
Year: 2017

With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. Users consent to providers to gather and share their Personally Identifiable Information (PII). We have developed a novel framework to automatically track details about how a users' PII data is stored, used and shared by the provider. We have integrated our Data Privacy ontology with the properties of blockchain, to develop an automated access control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by Big Data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.
Bodily aware soft robots: integration of proprioceptive and exteroceptive sensors
Venue: arXiv cs.RO 2017
Year: 2017

Being aware of our body has great importance in our everyday life. This is the reason why we know how to move in a dark room or to grasp a complex object. These skills are important for robots as well, however, robotic bodily awareness is still an unsolved problem. In this paper we present a novel method to implement bodily awareness in soft robots by the integration of exteroceptive and proprioceptive sensors. We use a combination of a stacked convolutional autoencoder and a recurrent neural network to map internal sensory signals to visual information. As a result, the simulated soft robot can learn to \textit{imagine} its motion even when its visual sensor is not available.
Toward Crowd-Sensitive Path Planning
Venue: arXiv cs.AI 2017
Year: 2017

If a robot can predict crowds in parts of its environment that are inaccessible to its sensors, then it can plan to avoid them. This paper proposes a fast, online algorithm that learns average crowd densities in different areas. It also describes how these densities can be incorporated into existing navigation architectures. In simulation across multiple challenging crowd scenarios, the robot reaches its target faster, travels less, and risks fewer collisions than if it were to plan with the traditional A* algorithm.
Which is better? A Modularized Evaluation for Topic Popularity Prediction
Venue: arXiv cs.IR 2017
Year: 2017

Topic popularity prediction in social networks has drawn much attention recently. Various elegant models have been proposed for this issue. However, different datasets and evaluation metrics they use lead to low comparability. So far there is no unified scheme to evaluate them, making it difficult to select and compare models. We conduct a comprehensible survey, propose an evaluation scheme and apply it to existing methods. Our scheme consists of four modules: classification; qualitative evaluation on several metrics; quantitative experiment on real world data; final ranking with risk matrix and $\textit{MinDis}$ to reflect performances under different scenarios. Furthermore, we analyze the efficiency and contribution of features used in feature oriented methods. The results show that feature oriented methods are more suitable for scenarios requiring high accuracy, while relation based methods have better consistency. Our work helps researchers compare and choose methods appropriately, and provides insights for further improvements.
Modulo Counting on Words and Trees
Venue: arXiv cs.LO 2017
Year: 2017

We consider the satisfiability problem for the two-variable fragment of the first-order logic extended with modulo counting quantifiers and interpreted over finite words or trees. We prove a small-model property of this logic, which gives a technique for deciding the satisfiability problem. In the case of words this gives a new proof of EXPSPACE upper bound, and in the case of trees it gives a 2EXPTIME algorithm. This algorithm is optimal: we prove a matching lower bound by a generic reduction from alternating Turing machines working in exponential space; the reduction involves a development of a new version of tiling games.
Quantitative Graph Theory: A new branch of graph theory and in network science
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we describe {\sc quantitative graph theory} and argue it is a new graph-theoretical branch in network science, however, with significant different features compared to classical graph theory. The main goal of quantitative graph theory is the structural quantification of information contained in complex networks by employing a {\it measurement approach} based on numerical invariants and comparisons. Furthermore, the methods as well as the networks do not need to be deterministic but can be statistic. As such this complements the field of classical graph theory, which is descriptive and deterministic in nature. We provide examples of how quantitative graph theory can be used for novel applications in the context of the overarching concept network science.
FlowCover: Low-cost Flow Monitoring Scheme in Software Defined Networks
Venue: arXiv cs.NI 2017
Year: 2017

Network monitoring and measurement are crucial in network management to facilitate quality of service routing and performance evaluation. Software Defined Networking (SDN) makes network management easier by separating the control plane and data plane. Network monitoring in SDN is lightweight as operators only need to install a monitoring module into the controller. Active monitoring techniques usually introduce too many overheads into the network. The state-of-the-art approaches utilize sampling method, aggregation flow statistics and passive measurement techniques to reduce overheads. However, little work in literature has focus on reducing the communication cost of network monitoring. Moreover, most of the existing approaches select the polling switch nodes by sub-optimal local heuristics. Inspired by the visibility and central control of SDN, we propose FlowCover, a low-cost high-accuracy monitoring scheme to support various network management tasks. We leverage the global view of the network topology and active flows to minimize the communication cost by formulating the problem as a weighted set cover, which is proved to be NP-hard. Heuristics are presented to obtain the polling scheme efficiently and handle flow changes practically. We build a simulator to evaluate the performance of FlowCover. Extensive experiment results show that FlowCover reduces roughly 50% communication cost without loss of accuracy in most cases.
A Hierarchy of Scheduler Classes for Stochastic Automata
Venue: arXiv cs.LO 2017
Year: 2017

Stochastic automata are a formal compositional model for concurrent stochastic timed systems, with general distributions and non-deterministic choices. Measures of interest are defined over schedulers that resolve the nondeterminism. In this paper we investigate the power of various theoretically and practically motivated classes of schedulers, considering the classic complete-information view and a restriction to non-prophetic schedulers. We prove a hierarchy of scheduler classes w.r.t. unbounded probabilistic reachability. We find that, unlike Markovian formalisms, stochastic automata distinguish most classes even in this basic setting. Verification and strategy synthesis methods thus face a tradeoff between powerful and efficient classes. Using lightweight scheduler sampling, we explore this tradeoff and demonstrate the concept of a useful approximative verification technique for stochastic automata.
Adaptive Full Duplex Communications in Cognitive Radio Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper we propose a novel adaptive scheme for full duplex communication of secondary users (SUs) in a cognitive radio network. The secondary network operates in three modes; Cooperative Sensing (CS), Full Duplex Transmit and Sensing (FDTS), and Full Duplex Transmit and Receive (FDTR). In the CS mode, the secondary nodes detect the activity of primary users (PUs) through a novel cooperative MAC protocol and will decide the systems mode of operation in the subsequent spectrum hole. In the FDTS mode one of the SUs senses the PUs activity continuously whilst transmitting to another node. In the FDTR mode, the SUs would communicate bidirectionally in an asynchronous full duplex (FD) manner, with decreased maximum and average collision durations. Analytical closed forms for probability of collision, average collision duration and cumulative collision duration, as well as throughput of the SU network are derived, and performance of the proposed protocol in terms of above-mentioned metrics, its effectiveness, and advantages over conventional methods of sensing and transmission are verified via simulations